Overview

Dataset statistics

Number of variables10
Number of observations768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.1 KiB
Average record size in memory80.2 B

Variable types

NUM9
BOOL1

Warnings

df_index has unique values Unique
Pregnant has 111 (14.5%) zeros Zeros
Insulin has 138 (18.0%) zeros Zeros

Reproduction

Analysis started2020-11-06 11:41:09.103455
Analysis finished2020-11-06 11:41:28.105702
Duration19 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct768
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean383.5
Minimum0
Maximum767
Zeros1
Zeros (%)0.1%
Memory size6.0 KiB
2020-11-06T08:41:28.286205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.35
Q1191.75
median383.5
Q3575.25
95-th percentile728.65
Maximum767
Range767
Interquartile range (IQR)383.5

Descriptive statistics

Standard deviation221.846794
Coefficient of variation (CV)0.5784792542
Kurtosis-1.2
Mean383.5
Median Absolute Deviation (MAD)192
Skewness0
Sum294528
Variance49216
MonotocityNot monotonic
2020-11-06T08:41:28.494794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
76710.1%
 
76610.1%
 
26110.1%
 
26010.1%
 
25910.1%
 
25810.1%
 
25710.1%
 
25610.1%
 
25510.1%
 
25410.1%
 
Other values (758)75898.7%
 
ValueCountFrequency (%) 
010.1%
 
110.1%
 
210.1%
 
310.1%
 
410.1%
 
ValueCountFrequency (%) 
76710.1%
 
76610.1%
 
76510.1%
 
76410.1%
 
76310.1%
 

Pregnant
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.845052083
Minimum0
Maximum17
Zeros111
Zeros (%)14.5%
Memory size6.0 KiB
2020-11-06T08:41:28.682057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.369578063
Coefficient of variation (CV)0.8763413316
Kurtosis0.1592197775
Mean3.845052083
Median Absolute Deviation (MAD)2
Skewness0.9016739792
Sum2953
Variance11.35405632
MonotocityNot monotonic
2020-11-06T08:41:28.859516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
113517.6%
 
011114.5%
 
210313.4%
 
3759.8%
 
4688.9%
 
5577.4%
 
6506.5%
 
7455.9%
 
8384.9%
 
9283.6%
 
Other values (7)587.6%
 
ValueCountFrequency (%) 
011114.5%
 
113517.6%
 
210313.4%
 
3759.8%
 
4688.9%
 
ValueCountFrequency (%) 
1710.1%
 
1510.1%
 
1420.3%
 
13101.3%
 
1291.2%
 

Glucose
Real number (ℝ≥0)

Distinct135
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.6770833
Minimum44
Maximum199
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2020-11-06T08:41:29.051529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile80
Q199.75
median117
Q3140.25
95-th percentile181
Maximum199
Range155
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation30.46416059
Coefficient of variation (CV)0.2503689253
Kurtosis-0.2681052005
Mean121.6770833
Median Absolute Deviation (MAD)20
Skewness0.532324244
Sum93448
Variance928.0650804
MonotocityNot monotonic
2020-11-06T08:41:29.274075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
99172.2%
 
100172.2%
 
129141.8%
 
106141.8%
 
107141.8%
 
111141.8%
 
125141.8%
 
105131.7%
 
108131.7%
 
112131.7%
 
Other values (125)62581.4%
 
ValueCountFrequency (%) 
4410.1%
 
5610.1%
 
5720.3%
 
6110.1%
 
6210.1%
 
ValueCountFrequency (%) 
19910.1%
 
19810.1%
 
19740.5%
 
19630.4%
 
19520.3%
 

BloodPressure
Real number (ℝ≥0)

Distinct46
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.37890625
Minimum24
Maximum122
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2020-11-06T08:41:29.505477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile52
Q164
median72
Q380
95-th percentile90
Maximum122
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.10443141
Coefficient of variation (CV)0.1672370037
Kurtosis1.088083343
Mean72.37890625
Median Absolute Deviation (MAD)8
Skewness0.1434132121
Sum55587
Variance146.5172598
MonotocityNot monotonic
2020-11-06T08:41:29.715346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
70769.9%
 
74688.9%
 
68455.9%
 
78455.9%
 
72445.7%
 
64435.6%
 
80405.2%
 
76395.1%
 
60374.8%
 
62344.4%
 
Other values (36)29738.7%
 
ValueCountFrequency (%) 
2410.1%
 
3020.3%
 
3810.1%
 
4010.1%
 
4440.5%
 
ValueCountFrequency (%) 
12210.1%
 
11410.1%
 
11030.4%
 
10820.3%
 
10630.4%
 

SkinThickness
Real number (ℝ≥0)

Distinct50
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.43098958
Minimum7
Maximum99
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2020-11-06T08:41:29.909963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14.35
Q121
median27
Q332
95-th percentile44
Maximum99
Range92
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.321460459
Coefficient of variation (CV)0.3398149539
Kurtosis4.392371469
Mean27.43098958
Median Absolute Deviation (MAD)6
Skewness1.128742746
Sum21067
Variance86.8896251
MonotocityNot monotonic
2020-11-06T08:41:30.136244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2114919.4%
 
2711114.5%
 
32314.0%
 
30273.5%
 
23222.9%
 
28202.6%
 
33202.6%
 
18202.6%
 
31192.5%
 
19182.3%
 
Other values (40)33143.1%
 
ValueCountFrequency (%) 
720.3%
 
820.3%
 
1050.7%
 
1160.8%
 
1270.9%
 
ValueCountFrequency (%) 
9910.1%
 
6310.1%
 
6010.1%
 
5610.1%
 
5420.3%
 

Insulin
Real number (ℝ≥0)

ZEROS

Distinct187
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.78385417
Minimum0
Maximum846
Zeros138
Zeros (%)18.0%
Memory size6.0 KiB
2020-11-06T08:41:30.345708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139
median39
Q3127.25
95-th percentile293
Maximum846
Range846
Interquartile range (IQR)88.25

Descriptive statistics

Standard deviation108.1211361
Coefficient of variation (CV)1.177997341
Kurtosis8.901933955
Mean91.78385417
Median Absolute Deviation (MAD)39
Skewness2.524252679
Sum70490
Variance11690.18008
MonotocityNot monotonic
2020-11-06T08:41:30.531340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3923630.7%
 
013818.0%
 
105111.4%
 
14091.2%
 
13091.2%
 
12081.0%
 
9470.9%
 
10070.9%
 
18070.9%
 
11560.8%
 
Other values (177)33043.0%
 
ValueCountFrequency (%) 
013818.0%
 
1410.1%
 
1510.1%
 
1610.1%
 
1820.3%
 
ValueCountFrequency (%) 
84610.1%
 
74410.1%
 
68010.1%
 
60010.1%
 
57910.1%
 

BMI
Real number (ℝ≥0)

Distinct249
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.43391927
Minimum18.2
Maximum67.1
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2020-11-06T08:41:30.716435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.235
Q127.5
median32.05
Q336.6
95-th percentile44.395
Maximum67.1
Range48.9
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.880664091
Coefficient of variation (CV)0.2121440839
Kurtosis0.9153719011
Mean32.43391927
Median Absolute Deviation (MAD)4.55
Skewness0.6066504576
Sum24909.25
Variance47.34353834
MonotocityNot monotonic
2020-11-06T08:41:30.907225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
32131.7%
 
31.2121.6%
 
31.6121.6%
 
32.4101.3%
 
33.3101.3%
 
32.891.2%
 
30.191.2%
 
30.0591.2%
 
32.991.2%
 
30.891.2%
 
Other values (239)66686.7%
 
ValueCountFrequency (%) 
18.230.4%
 
18.410.1%
 
19.110.1%
 
19.310.1%
 
19.410.1%
 
ValueCountFrequency (%) 
67.110.1%
 
59.410.1%
 
57.310.1%
 
5510.1%
 
53.210.1%
 

DiabetesPedigree
Real number (ℝ≥0)

Distinct517
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4718763021
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2020-11-06T08:41:31.112919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.14035
Q10.24375
median0.3725
Q30.62625
95-th percentile1.13285
Maximum2.42
Range2.342
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.331328595
Coefficient of variation (CV)0.7021513764
Kurtosis5.594953528
Mean0.4718763021
Median Absolute Deviation (MAD)0.1675
Skewness1.919911066
Sum362.401
Variance0.1097786379
MonotocityNot monotonic
2020-11-06T08:41:31.318976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.25460.8%
 
0.25860.8%
 
0.25950.7%
 
0.20750.7%
 
0.26150.7%
 
0.26850.7%
 
0.23850.7%
 
0.28440.5%
 
0.68740.5%
 
0.2740.5%
 
Other values (507)71993.6%
 
ValueCountFrequency (%) 
0.07810.1%
 
0.08410.1%
 
0.08520.3%
 
0.08820.3%
 
0.08910.1%
 
ValueCountFrequency (%) 
2.4210.1%
 
2.32910.1%
 
2.28810.1%
 
2.13710.1%
 
1.89310.1%
 

Age
Real number (ℝ≥0)

Distinct52
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.24088542
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2020-11-06T08:41:31.513875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.76023154
Coefficient of variation (CV)0.3537881556
Kurtosis0.6431588885
Mean33.24088542
Median Absolute Deviation (MAD)7
Skewness1.129596701
Sum25529
Variance138.3030459
MonotocityNot monotonic
2020-11-06T08:41:31.705176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
22729.4%
 
21638.2%
 
25486.2%
 
24466.0%
 
23384.9%
 
28354.6%
 
26334.3%
 
27324.2%
 
29293.8%
 
31243.1%
 
Other values (42)34845.3%
 
ValueCountFrequency (%) 
21638.2%
 
22729.4%
 
23384.9%
 
24466.0%
 
25486.2%
 
ValueCountFrequency (%) 
8110.1%
 
7210.1%
 
7010.1%
 
6920.3%
 
6810.1%
 

Outcome
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
0
500 
1
268 
ValueCountFrequency (%) 
050065.1%
 
126834.9%
 
2020-11-06T08:41:31.843765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Interactions

2020-11-06T08:41:12.240496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:12.459060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:12.646174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:12.845556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:13.049601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:13.256510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:13.438788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:13.620054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:13.805964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:13.999975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:14.208835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:14.395853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:14.590800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:14.771984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:15.063209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:15.246080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:15.436619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:15.637568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:15.821046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:16.014202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:16.215273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:16.427454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:16.612811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:16.807929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:16.984682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:17.174899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:17.370772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:17.567276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:17.744629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:17.932093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:18.153840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:18.326242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:18.491265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:18.689781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:18.867455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:19.039160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:19.214825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:19.390509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:19.580185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:19.755719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:19.914926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:20.070805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:20.229273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:20.379425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:20.536294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:20.818464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:20.985625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:21.154647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:21.329067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:21.522649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:21.686983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:21.837167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:21.987796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:22.157671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:22.337851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:22.510252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:22.677325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:22.846468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:23.003268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:23.186492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:23.344036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:23.497413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:23.675010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:23.858819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:24.044829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:24.236120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:24.425987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:24.592683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:24.771318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:24.954044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:25.130244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:25.298646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:25.465945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:25.670080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:25.864589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:26.075730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:26.264183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:26.440706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:26.606009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:26.796693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:26.988831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-06T08:41:31.938036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-06T08:41:32.190491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-06T08:41:32.427121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-06T08:41:32.887609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-06T08:41:27.331645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-06T08:41:27.910542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexPregnantGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeAgeOutcome
004.0117.062.012.00.029.70.38030.01
114.0158.078.027.00.032.90.80331.01
222.0118.080.027.00.042.90.69321.01
3313.0129.074.030.00.039.90.56944.01
445.0162.0104.027.00.037.70.15152.01
557.0114.064.027.00.027.40.73234.01
666.0102.082.027.00.030.80.18036.01
771.0196.076.036.0249.036.50.87529.01
889.0102.076.037.00.032.90.66546.01
997.0161.086.027.00.030.40.16547.01

Last rows

df_indexPregnantGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeAgeOutcome
7587585.0132.080.021.039.026.80.18669.00
7597599.091.068.021.039.024.20.20058.00
7607603.0128.078.021.039.021.10.26855.00
7617610.0108.068.020.039.027.30.78732.00
7627622.0112.068.022.094.034.10.31526.00
7637631.081.074.041.057.046.31.09632.00
7647644.094.065.022.039.024.70.14821.00
7657653.0158.064.013.0387.031.20.29524.00
7667660.057.060.021.039.021.70.73567.00
7677674.095.060.032.039.035.40.28428.00